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August 12, 2025Classical and Quantum Gravity

Advancing Glitch Classification in Gravity Spy: Multi-view Fusion with Attention-based Machine Learning for Advanced LIGO's Fourth Observing Run

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Authors

YWYunan WuMZM. ZevinCBC. P. L. Berry

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Overview

Advanced classifier improves glitch detection in LIGO data by integrating multi-time window machine learning techniques.

Key Points

  • Improved performance of the new classifier enhances glitch classification in ongoing LIGO observing runs, aiding gravitational-wave research.
  • The advanced classifier utilized multi-time window inputs to manage complexity, resolving issues faced in previous architectures.
  • Analysis incorporates attention-based strategies and label smoothing to counteract the effects of noisy labels on performance.
  • The Gravity Spy project benefits from community science and machine learning to address challenges in gravitational-wave detection.

Cite This Study

Wu et al. (2025) studied this question.

synapsesocial.com/papers/68a360f20a429f79733299e7https://doi.org/10.1088/1361-6382/adf58b
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Also Consider

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  4. 4Cross-Temporal Spectrogram Autoencoder (CTSAE): Unsupervised Dimensionality Reduction for Clustering Gravitational Wave Glitches2024
  5. 5Convolutional neural networks for signal detection in real LIGO data2024 · 7 citations